Abstract Metabolic syndrome is a cluster of conditions (increased blood pressure, high blood sugar, excess body fat around the waist, and abnormal cholesterol or triglyceride levels) that occur together, increasing risk of heart disease, stroke and diabetes. Epidemiological studies have shown that prolonged sitting is deleterious to metabolic indicators, even after adjusting for physical activity (PA). Acute laboratory trials have shown that breaking up sitting time can improve metabolic factors. Sitting is a prevalent behavior in all population groups by age, gender and ethnicity. Associations with metabolic syndrome factors, such as obesity, have also been shown in all population groups. Epidemiological studies have mostly depended on reported sitting time, especially TV reviewing. More recently large cohort studies have collected data from hip worn accelerometers and applied a cut point (e.g., 100 counts per minute) on single axis data to estimate sedentary time. Such devices have been included in numerous studies, principally because of their accuracy to measure PA intensity. Primarily used in intervention trials to reduce sitting, the thigh worn ActivPAL has been shown to more accurately assess posture and provide valid measures of sitting, standing, and sit-stand transitions. To date, very few health outcome cohort studies have included the ActivPAL. Compared to the ActivPAL and free living observations of sitting time, the 100 count cut point has been shown to underestimate prolonged sitting by substantially overestimating sit-stand transitions. New studies are showing that how we accumulate sitting time (i.e. in long or short bouts) is associated with metabolic health outcomes, and may be independent of total sitting time and PA. Study results on prolonged sitting and metabolic risk factors from accelerometer data are inconsistent and may be due to measurement error in the cut points employed. In a small sample of older women, adults, and youth we have demonstrated that novel machine learned methods can greatly improve estimates of prolonged sitting and transitions. Further development and testing of these methods would support valid applications to existing large cohort studies with raw accelerometer data to improve estimates of associations between sitting patterns and metabolic health. There are also many large cohorts (e.g. NHANES 2003/6), with quality health outcomes, but non raw accelerometer count data, so calibration methods to adjust non raw estimates of sitting time are also needed and would be attractive to researchers not yet familiar with the machine learning process. We proposed to employ 7 existing data sets (N=20,000) matched for age and spanning youth, adults and older adults. We will scale up our training and test the performance of the refined algorithms to detect sit-stand frequencies, prolonged sitting, usual bout duration and Alpha (a combination of duration & frequency). We will test performance of the algorithms against ActivPAL (ground truth) and in new samples assess predictive validity with objective health outcomes. We will test differences between the existing and new techniques using R2 and mean-squared error of prediction (via bootstrapping) and GEE techniques.